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train · 2.93k rows

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A: In this paper, we examine various concepts of clones of operations and relations on a given base set A𝐴Aitalic_AB: Unlike classic clone theory, which limits the arities of functions and relations to be finite, our study allows for arity ω𝜔\omegaitalic_ω for both operations and relationsC: Additionally...	BCA	BAC	CAB	ABC	Selection 4
A: One way of dealing with this issue is to present all optimal solutions of the simplified model and let a user choose between them based on external factors ignored by the mathematical modelB: Another approach is to present only a small number k𝑘kitalic_k of optimal solutions, but one should be careful not t...	CBA	BCA	ACB	BCA	Selection 3
A: We also show how to lower bound information between probabilities over general spaces with information between probabilities over finite sequences using uniformly enumerable disjoint open setsB: We provide an means to upper bound the probabilities between general spaces using computable non-probabilistic me...	CBA	BCA	ABC	BCA	Selection 3
A: CN-Patches can be used as layer on Transformer-based architecture [12] like Vision Transformer (ViT) [13]. ViT model takes as input a grid of non-overlapping contiguous image patchesB: CN-Patches consist of normalizing image patches with a vector μ𝜇\muitalic_μ and σ𝜎\sigmaitalic_σ that are learned from the...	BAC	CAB	ABC	BAC	Selection 3
A: Four post-hoc foreground OOD detection methods are respectively plugged into our method ‘X’-DFB, where improved results are highlighted in red and they are in blue otherwise. The best result per dataset is boldfaced. B: All methods are based on ID training data without using any external outlier dataC: ...	CAB	CBA	BCA	BCA	Selection 1
A: Other notable modifications from the original U-Net are the use of attention mechanisms at different spatial resolutions as well as the use of group normalization [46] within the residual blocks. As seen in Fig. 2, the residual blocks of the DM are composed of a combination of group normalization layers, SiLU ac...	BAC	ACB	BAC	CAB	Selection 4
A: 2f-g). Furthermore, our findings indicate a significant performance improvement among participants in the timed games, while such improvement was not evident in the games constrained by distance (as indicated by the Order variable).B: 2d-e). Conversely, among participants who chose to play games with distanc...	CAB	CBA	ACB	BAC	Selection 2
A: These sequence models inherently encapsulate rich structural information gleaned from sequence data, enabling them to effectively guide structure model learningB: Pretrained protein language models have become integral to deep protein downstream tasks, having been trained on extensive datasetsC: Among th...	BCA	ACB	BCA	BAC	Selection 4
A: Second, tensor factorization methods, e.g., RESCAL [18], model the knowledge graph as a three-way tensor, and apply tensor decomposition to obtain entity/relation representationsB: Later methods further improve the effectiveness of decompositionC: For example, DistMult [19] applies an efficient diagonal ...	ABC	BAC	BCA	ACB	Selection 1
A: In this situation, the algorithm has found an alternative path to reach the neighboring node and has to check whether this new path is dominated or not by the old one. To do this, temporary registry is required to keep track of the optimal fidelity curve found for each node, alongside the respective paths (as re...	ABC	ACB	BAC	ACB	Selection 3
A: [12] propose the polarization weight (PW) algorithm exploiting the index expansion of synthetic channels to design a universal reliability sequence without the channel parametersB: Then, He et alC: However, the PW sequence has performance loss for polar codes with the code length N≥1024𝑁1024N\geq 1024it...	CBA	ACB	BAC	ABC	Selection 3
A: So for this tree we have that the mean depth is given byB: where the inequality follows from our assumption 1/2<p<112𝑝11/2<p<11 / 2 < italic_p < 1C: Thus we see that when there are signals the weights on leaf nodes are no longer proportional, but skewed further towards the longer branches	BAC	BAC	CBA	CAB	Selection 4
A: However, practical challenges, such as ethical and legal impediments to sharing medical data, particularly for unstructured radiology reports, complicate this endeavor [Scheibner et al. (2021), Bovenberg et alB: (2020)]. For one of the few public datasets of this caliber that exists, MIMIC-CXR, Chambon et al...	CAB	BAC	ABC	CAB	Selection 3
A: On the other hand, 3D layout-based approaches, exemplified by NSG [37] and its successors [48, 9], focus on spatial coordinates, using explicit 3D object placement data to guide object and scene composition. Diverging from conventional techniques, our method innovates by utilizing decomposed, object-specific 3D ...	CAB	BCA	CBA	CAB	Selection 2
A: Table 2 shows the results for K-Means, hierarchical, spectral, and EM-GMM. All these algorithms receive the true number of clusters as an input. B: We measure performance in terms of adjusted mutual information (AMI) and adjusted Rand index (ARI), based on the ground truth cluster labels (Vinh et al., (2010)...	BCA	CBA	CAB	BCA	Selection 3
A: In this study, we propose a novel Contrastive Oracle-Free Framework for Event Extraction (COFFEE), which addresses the event extraction task without using any oracle information. Our COFFEE consists of two parts, a generator that performs the extraction of events and a selector that aims to refine the generated ...	BCA	CBA	ABC	ACB	Selection 4
A: As mentioned in Fischer and Steinwart (2020), the empirical process and the integral operator techniques are the two main techniques used to derive the learning rates of kernel methods. Steinwart et alB: Our paper further strengthens the integral operator technique, making it the most powerful technique for ...	ACB	ABC	BAC	ABC	Selection 1
A: Finally, it has been observed that most of the aforementioned works on feature-preserving point cloud simplification schemes experiment on structurally simple point cloudsB: Hence, to underline the efficiency of our method, we experiment on point clouds generated from complex-surfaced objects and provide th...	CAB	ACB	ABC	CBA	Selection 2
A: The rest of this paper is organized as follows. We review the related work in Section IIB: System model and problem formulation are given in Section III. In Section IV, an analysis of the adaptive learning rate is presentedC: We provide the convergence analysis in Section V. Experimental results are sho...	CBA	ACB	BCA	ABC	Selection 4
A: In contrast, our LLaMA-Adapter achieves higher training efficiency and can be simply generalized to a multi-modal LLM via zero-initialized attention. Vicuna and LLaMA-GPT4 target at constructing a more advanced instruction dataset using ChatGPT (OpenAI, 2023a) and GPT-4 (OpenAI, 2023b), instead of Alpaca’s 52K d...	BCA	ACB	CBA	BAC	Selection 3
A: Moreover, we visualize effects of temporal grouping and spatial grounding in Figure 2B: Besides, attention maps from spatial grounding indicates the alignment between the region and the noun phrase has been learned during the pre-training stage without any fine-grained annotations. More examples can be found...	BCA	BCA	ACB	CAB	Selection 3
A: In the multilingual benchmark (Table 2, FAISS results), we found a much greater difference in accuracy between shallow and deep layers in ContraSim compared to previous similarity measures. Using previous similarity measures we might infer that there is no difference in the ability to detect the correct pairs a...	ACB	CAB	BCA	ABC	Selection 4
A: Four VP/ADPs of the labels in the front, right, top, and back-right-bottom (BRB) are shown in a unit sphere as an example of VP/ADPs.B: These orthogonal points are obtained as VP/ADPs without camera rotation; that is, pan, tilt, and roll angles are 0∘superscript00^{\circ}0 start_POSTSUPERSCRIPT ∘ end_POSTSUP...	BCA	ABC	BCA	CBA	Selection 4
A: Moreover, the proposed conditions also apply to systems where a direct input-to-output channel is present.B: We have provided necessary and sufficient conditions for the synchronization of identical linear SISO systems, with a guaranteed convergence rate, both in the continuous-time and in the discrete-time ...	ABC	CAB	ACB	BCA	Selection 2
A: studied behavioral issues resulting from framework-to-framework conversion (Louloudakis et al., 2023a). They found failures in 10 out of 36 conversionsB: They created a fault localization and repair pipeline to localize and fix discrepancies (Louloudakis et al., 2023b).C: Louloudakis et al	BAC	BCA	CAB	ACB	Selection 2
A: The objective of this experiment is not only to test the simulation with real capabilities of the framework in an indoor environment but also to test the framework’s platform independenceB: In this experiment, two different drones shall pass through two gates in a synchronized and cyclic fashionC: The e...	CBA	CAB	BCA	BAC	Selection 4
A: In fact, the current legal framework does not appear to be completely suited to the fast-moving field of generative AIB: Moreover, the impact of these technologies on creative professions and the arts is difficult to forecast at this stage, but will definitely be considerable.C: In addition, we have als...	CAB	BCA	CBA	CBA	Selection 2
A: Qualitative Comparison. We visualize the detection results of the compared SOTA approaches and our SUP-ICI in Fig. 4. The detection results are displayed based on the central slices of the nodule ground truthsB: On the contrary, the compared approaches’ detection results are usually offset or even completely...	ABC	BAC	ACB	CAB	Selection 3
A: To take uncertainties in the net-load into account,111In this paper, we use the term net-load to capture both renewable generation in the system [5] and the loadB: In these problems, decisions are made sequentially at each stage, based on the forecast of the net-load and the fact that additional adjustments ...	ABC	CAB	ACB	CBA	Selection 3
A: However, in this case, the unlabelled data is only used for evaluating the KL divergence in function space, a practice which has theoretically been shown to be insufficient Burt et al. (2020). Conversely, our work uses semantic information from the unlabelled data to actually inform the function-space prior.*B...	BAC	CBA	BAC	ACB	Selection 2
A: Outliers are common in genomic data, and many invariants from geometric data analysis are sensitive to outliers (e.g., see [11, §4])B: A suitable weight function can mitigate their effectC: However, a weight function that suppresses outliers à priori can increase the type II error since irregularities ar...	BCA	BAC	BCA	ABC	Selection 4

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